scholarly journals Tracking Moving Optima Using Kalman-Based Predictions

2008 ◽  
Vol 16 (1) ◽  
pp. 1-30 ◽  
Author(s):  
Claudio Rossi ◽  
Mohamed Abderrahim ◽  
Julio César Díaz
Keyword(s):  
Evolutionary Algorithms ◽  
Dynamic Optimization ◽  
Optimization Problem ◽  
Moving Objects ◽  
Fitness Landscape ◽  
Experimental Comparison ◽  
Motion Information ◽  
Dynamic Optimization Problem ◽  
First Order ◽  
Robotic Application

The dynamic optimization problem concerns finding an optimum in a changing environment. In the field of evolutionary algorithms, this implies dealing with a time-changing fitness landscape. In this paper we compare different techniques for integrating motion information into an evolutionary algorithm, in the case it has to follow a time-changing optimum, under the assumption that the changes follow a nonrandom law. Such a law can be estimated in order to improve the optimum tracking capabilities of the algorithm. In particular, we will focus on first order dynamical laws to track moving objects. A vision-based tracking robotic application is used as testbed for experimental comparison.

scholarly journals Swarm algorithms in dynamic optimization problem of reactive power compensation units control

2019 ◽  
Vol 9 (5) ◽  
pp. 3967
Author(s):  
V.Z Manusov ◽  
P.V. Matrenin ◽  
N. Khasanzoda
Keyword(s):  
Dynamic Optimization ◽  
Power Supply ◽  
Optimization Problem ◽  
Power Supply System ◽  
Reactive Power ◽  
Reactive Power Compensation ◽  
Supply System ◽  
Bees Algorithm ◽  
Dynamic Optimization Problem ◽  
Engineering Research

Optimization of a power supply system is one of the main directions in power engineering research. The reactive power compensation reduces active power losses in transmission lines. In general, researches devoted to allocation and control of the compensation units consider this issue as a static optimization problem. However, it is dynamic and stochastic optimization problem that requires a real-time solution. To solve the dynamic optimization NP-hard problem, it is advisable to use Swarm Intelligence. This research deals with the problem of the compensation units power control as a dynamic optimization problem, considering the possible stochastic failures of the compensation units. The Particle Swarm Optimization and the Bees Algorithm were applied to solve it to compare the effectiveness of these algorithms in the dynamic optimization of a power supply system.

scholarly journals An Optimization-Based Impedance Approach for Robot Force Regulation with Prescribed Force Limits

2015 ◽  
Vol 2015 ◽  
pp. 1-13 ◽  
Author(s):  
R. de J. Portillo-Vélez ◽  
A. Rodriguez-Angeles ◽  
C. A. Cruz-Villar
Keyword(s):  
Dynamic Optimization ◽  
Optimization Problem ◽  
Gradient Flow ◽  
Interaction Force ◽  
Convergence Properties ◽  
Dynamic Optimization Problem ◽  
Interaction Forces ◽  
End Effector ◽  
Impedance Model ◽  
The Stability

An optimization based approach for the regulation of excessive or insufficient forces at the end-effector level is introduced. The objective is to minimize the interaction force error at the robot end effector, while constraining undesired interaction forces. To that end, a dynamic optimization problem (DOP) is formulated considering a dynamic robot impedance model. Penalty functions are considered in the DOP to handle the constraints on the interaction force. The optimization problem is online solved through the gradient flow approach. Convergence properties are presented and the stability is drawn when the force limits are considered in the analysis. The effectiveness of our proposal is validated via experimental results for a robotic grasping task.

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